If you're an experienced ML developer and the pre-built TensorFlow Lite library doesn't meet your needs, you can use a custom TensorFlow Lite build with ML Kit. For example, you may want to add custom ops.
Prerequisites
- A working TensorFlow Lite build environment
Bundling a custom TensorFlow Lite for Android
Build the Tensorflow Lite AAR:
bazel build --cxxopt='--std=c++11' -c opt \ --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \ //tensorflow/lite/java:tensorflow-lite
This will generate an AAR file in bazel-genfiles/tensorflow/lite/java/
.
Publish the custom Tensorflow Lite AAR to your local
Maven repository:
mvn install:install-file -Dfile=bazel-genfiles/tensorflow/lite/java/tensorflow-lite.aar -DgroupId=org.tensorflow \ -DartifactId=tensorflow-lite -Dversion=0.1.100 -Dpackaging=aar
Finally, in your app build.gradle
, override Tensorflow Lite with your custom
version:
implementation 'org.tensorflow:tensorflow-lite:0.1.100'